Auto-adaptive learning-based workload forecasting in dynamic cloud environment

Abstract

To maintain elasticity and scalability of resources at cloud data centers, future workload prediction has become an indispensable requirement. However, there is high variance in resource demands, due to sudden peaks, drop of workload, noise and redundancy in user demands, that hinders accurate workload forecast. This article aims to present workload prediction adaptive neural network model to forecast average workload over consecutive prediction intervals in anticipation. The prediction model adaptively learns traces of workload for particular prediction interval from historical data by applying proposed novel Auto Adaptive Differential Evolution (AADE) algorithm. The two benchmark datasets viz. NASA and Saskatchewan HTTP traces are used for evaluating the performance of the proposed work. Experimental results reveal that AADE-trained neural network outperforms state-of-the-art schemes and achieves accuracy improvement upto 98.9%, 97.4% and 94.8% compared to Average, Backpropagation (BP) and Self adaptive Differential Evolution (SaDE) learning based workload prediction schemes respectively. In addition, the speed of convergence of AADE learning algorithm is observed to be 2-10 times faster than BP and SaDE based forecasting approaches for both datasets.

Keywords:

• Accuracy
• cloud computing
• data center
• differential evolution
• neural network
• workload forecasting

Acknowledgments

This work is financially supported by National Institute of Technology Kurukshetra, Haryana, India.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is financially supported by National Institute of Technology Kurukshetra, Haryana, India granted by ‘Ministry of Education, Government of India – MHRD’.

Notes on contributors

Deepika Saxena

Deepika Saxena received her M.Tech degree in Computer Science and Engineering from Kurukshetra University Kurukshetra, Haryana, India in 2014. Currently, she is pursuing her Ph.D from Department of Computer Applications, National Institute of Technology (NIT), Kurukshetra, India. Her major research interests are predictive analytics.evolutionary algorithms, scheduling and security in cloud computing.

Ashutosh Kumar Singh

Ashutosh Kumar Singh is working as a Professor and Head in the Department of Computer Applications, National Institute of Technology Kurukshetra, India. He has more than 18 years research and teaching experience in various Universities of the India, UK, and Malaysia. He received his PhD in Electronics Engineering from Indian Institute of Technology, BHU, India and Post Doc from Department of Computer Science, University of Bristol, UK. He is also Charted Engineer from UK. His research area includes Verification, Synthesis, Design and Testing of Digital Circuits, Data Science, Cloud Computing, Machine Learning, Security, Big Data. He has published more than 160 research papers in different journals, conferences and news magazines. He is the co-author of six books which includes ‘Web Spam Detection Application using Neural Network’, ‘Digital Systems Fundamentals’ and ‘Computer System Organization & Ar- chitecture’. He has worked as an Editorial Board Member of International Journal of Networks and Mobile Technologies, International journal of Digital Content Technology and its Applica- tions. Also he has shared his experience as a Guest Editor for Pertanika Journal of Science and Technology. He is involved in reviewing process of different journals and conferences such as; IEEE transaction of computer, IET, IEEE conference on ITC, ADCOM etc.